Overview

Dataset statistics

Number of variables19
Number of observations2862
Missing cells8640
Missing cells (%)15.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory425.0 KiB
Average record size in memory152.0 B

Variable types

Categorical8
Numeric11

Alerts

Unit has constant value "per 100,000 population"Constant
State has a high cardinality: 52 distinct valuesHigh cardinality
Deaths has a high cardinality: 1602 distinct valuesHigh cardinality
Population has a high cardinality: 2862 distinct valuesHigh cardinality
State Crude Rate in Range has a high cardinality: 60 distinct valuesHigh cardinality
Year is highly overall correlated with Age-adjusted Rate and 4 other fieldsHigh correlation
Crude Death Rate is highly overall correlated with Lower Confidence Limit for Crude Rate and 5 other fieldsHigh correlation
Standard Error for Crude Rate is highly overall correlated with Upper Confidence Limit for Crude Rate and 1 other fieldsHigh correlation
Lower Confidence Limit for Crude Rate is highly overall correlated with Crude Death Rate and 4 other fieldsHigh correlation
Upper Confidence Limit for Crude Rate is highly overall correlated with Crude Death Rate and 5 other fieldsHigh correlation
Age-adjusted Rate is highly overall correlated with Year and 9 other fieldsHigh correlation
Standard Error for Age-adjusted Rate is highly overall correlated with Standard Error for Crude Rate and 2 other fieldsHigh correlation
Lower Confidence Limit for Age-adjusted Rate is highly overall correlated with Year and 9 other fieldsHigh correlation
Upper Confidence Limit for Age-adjusted Rate is highly overall correlated with Year and 8 other fieldsHigh correlation
US Crude Rate is highly overall correlated with Year and 4 other fieldsHigh correlation
US Age-adjusted Rate is highly overall correlated with Year and 4 other fieldsHigh correlation
State is highly overall correlated with Standard Error for Age-adjusted RateHigh correlation
Sex is highly overall correlated with State Crude Rate in RangeHigh correlation
Age Group is highly overall correlated with Age-adjusted Rate and 4 other fieldsHigh correlation
Race and Hispanic Origin is highly overall correlated with State Crude Rate in RangeHigh correlation
State Crude Rate in Range is highly overall correlated with Crude Death Rate and 5 other fieldsHigh correlation
State is highly imbalanced (52.2%)Imbalance
Age-adjusted Rate has 1728 (60.4%) missing valuesMissing
Standard Error for Age-adjusted Rate has 1728 (60.4%) missing valuesMissing
Lower Confidence Limit for Age-adjusted Rate has 1728 (60.4%) missing valuesMissing
Upper Confidence Limit for Age-adjusted Rate has 1728 (60.4%) missing valuesMissing
State Crude Rate in Range has 1728 (60.4%) missing valuesMissing
Population is uniformly distributedUniform
Population has unique valuesUnique

Reproduction

Analysis started2023-09-18 22:49:27.330270
Analysis finished2023-09-18 22:50:06.578670
Duration39.25 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

State
Categorical

HIGH CARDINALITY  HIGH CORRELATION  IMBALANCE 

Distinct52
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
United States
1944 
Alabama
 
18
Pennsylvania
 
18
Nevada
 
18
New Hampshire
 
18
Other values (47)
846 

Length

Max length20
Median length13
Mean length11.610063
Min length4

Characters and Unicode

Total characters33228
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlabama
2nd rowAlabama
3rd rowAlabama
4th rowAlabama
5th rowAlabama

Common Values

ValueCountFrequency (%)
United States 1944
67.9%
Alabama 18
 
0.6%
Pennsylvania 18
 
0.6%
Nevada 18
 
0.6%
New Hampshire 18
 
0.6%
New Jersey 18
 
0.6%
New Mexico 18
 
0.6%
New York 18
 
0.6%
North Carolina 18
 
0.6%
North Dakota 18
 
0.6%
Other values (42) 756
 
26.4%

Length

2023-09-18T22:50:06.772774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united 1944
38.7%
states 1944
38.7%
new 72
 
1.4%
north 36
 
0.7%
dakota 36
 
0.7%
carolina 36
 
0.7%
virginia 36
 
0.7%
south 36
 
0.7%
delaware 18
 
0.4%
district 18
 
0.4%
Other values (47) 846
16.8%

Most occurring characters

ValueCountFrequency (%)
t 6174
18.6%
e 4392
13.2%
a 2988
9.0%
i 2700
8.1%
n 2574
7.7%
s 2502
7.5%
2160
 
6.5%
d 2088
 
6.3%
S 1980
 
6.0%
U 1962
 
5.9%
Other values (36) 3708
11.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26064
78.4%
Uppercase Letter 5004
 
15.1%
Space Separator 2160
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 6174
23.7%
e 4392
16.9%
a 2988
11.5%
i 2700
10.4%
n 2574
9.9%
s 2502
9.6%
d 2088
 
8.0%
o 630
 
2.4%
r 396
 
1.5%
l 270
 
1.0%
Other values (14) 1350
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
S 1980
39.6%
U 1962
39.2%
M 162
 
3.2%
N 144
 
2.9%
C 108
 
2.2%
I 90
 
1.8%
D 72
 
1.4%
W 72
 
1.4%
A 72
 
1.4%
O 54
 
1.1%
Other values (11) 288
 
5.8%
Space Separator
ValueCountFrequency (%)
2160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31068
93.5%
Common 2160
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 6174
19.9%
e 4392
14.1%
a 2988
9.6%
i 2700
8.7%
n 2574
8.3%
s 2502
8.1%
d 2088
 
6.7%
S 1980
 
6.4%
U 1962
 
6.3%
o 630
 
2.0%
Other values (35) 3078
9.9%
Common
ValueCountFrequency (%)
2160
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 6174
18.6%
e 4392
13.2%
a 2988
9.0%
i 2700
8.1%
n 2574
7.7%
s 2502
7.5%
2160
 
6.5%
d 2088
 
6.3%
S 1980
 
6.0%
U 1962
 
5.9%
Other values (36) 3708
11.2%

Year
Real number (ℝ)

Distinct18
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.5
Minimum1999
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-18T22:50:07.013262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1999
5-th percentile1999
Q12003
median2007.5
Q32012
95-th percentile2016
Maximum2016
Range17
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.1890341
Coefficient of variation (CV)0.002584824
Kurtosis-1.207443
Mean2007.5
Median Absolute Deviation (MAD)4.5
Skewness0
Sum5745465
Variance26.926075
MonotonicityNot monotonic
2023-09-18T22:50:07.367239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1999 159
 
5.6%
2000 159
 
5.6%
2015 159
 
5.6%
2014 159
 
5.6%
2013 159
 
5.6%
2012 159
 
5.6%
2011 159
 
5.6%
2010 159
 
5.6%
2009 159
 
5.6%
2008 159
 
5.6%
Other values (8) 1272
44.4%
ValueCountFrequency (%)
1999 159
5.6%
2000 159
5.6%
2001 159
5.6%
2002 159
5.6%
2003 159
5.6%
2004 159
5.6%
2005 159
5.6%
2006 159
5.6%
2007 159
5.6%
2008 159
5.6%
ValueCountFrequency (%)
2016 159
5.6%
2015 159
5.6%
2014 159
5.6%
2013 159
5.6%
2012 159
5.6%
2011 159
5.6%
2010 159
5.6%
2009 159
5.6%
2008 159
5.6%
2007 159
5.6%

Sex
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
Both Sexes
1566 
Male
648 
Female
648 

Length

Max length10
Median length10
Mean length7.7358491
Min length4

Characters and Unicode

Total characters22140
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBoth Sexes
2nd rowBoth Sexes
3rd rowBoth Sexes
4th rowBoth Sexes
5th rowBoth Sexes

Common Values

ValueCountFrequency (%)
Both Sexes 1566
54.7%
Male 648
22.6%
Female 648
22.6%

Length

2023-09-18T22:50:07.775843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-18T22:50:08.250016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
both 1566
35.4%
sexes 1566
35.4%
male 648
14.6%
female 648
14.6%

Most occurring characters

ValueCountFrequency (%)
e 5076
22.9%
B 1566
 
7.1%
o 1566
 
7.1%
t 1566
 
7.1%
h 1566
 
7.1%
1566
 
7.1%
S 1566
 
7.1%
x 1566
 
7.1%
s 1566
 
7.1%
a 1296
 
5.9%
Other values (4) 3240
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16146
72.9%
Uppercase Letter 4428
 
20.0%
Space Separator 1566
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5076
31.4%
o 1566
 
9.7%
t 1566
 
9.7%
h 1566
 
9.7%
x 1566
 
9.7%
s 1566
 
9.7%
a 1296
 
8.0%
l 1296
 
8.0%
m 648
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
B 1566
35.4%
S 1566
35.4%
M 648
14.6%
F 648
14.6%
Space Separator
ValueCountFrequency (%)
1566
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20574
92.9%
Common 1566
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5076
24.7%
B 1566
 
7.6%
o 1566
 
7.6%
t 1566
 
7.6%
h 1566
 
7.6%
S 1566
 
7.6%
x 1566
 
7.6%
s 1566
 
7.6%
a 1296
 
6.3%
l 1296
 
6.3%
Other values (3) 1944
 
9.4%
Common
ValueCountFrequency (%)
1566
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5076
22.9%
B 1566
 
7.1%
o 1566
 
7.1%
t 1566
 
7.1%
h 1566
 
7.1%
1566
 
7.1%
S 1566
 
7.1%
x 1566
 
7.1%
s 1566
 
7.1%
a 1296
 
5.9%
Other values (4) 3240
14.6%

Age Group
Categorical

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
All Ages
1134 
15–24
216 
0–14
216 
25–34
216 
35–44
216 
Other values (4)
864 

Length

Max length8
Median length5
Mean length5.8867925
Min length2

Characters and Unicode

Total characters16848
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll Ages
2nd rowAll Ages
3rd rowAll Ages
4th rowAll Ages
5th rowAll Ages

Common Values

ValueCountFrequency (%)
All Ages 1134
39.6%
15–24 216
 
7.5%
0–14 216
 
7.5%
25–34 216
 
7.5%
35–44 216
 
7.5%
45–54 216
 
7.5%
55–64 216
 
7.5%
65–74 216
 
7.5%
75 216
 
7.5%

Length

2023-09-18T22:50:08.635342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-18T22:50:09.130838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
all 1134
28.4%
ages 1134
28.4%
15–24 216
 
5.4%
0–14 216
 
5.4%
25–34 216
 
5.4%
35–44 216
 
5.4%
45–54 216
 
5.4%
55–64 216
 
5.4%
65–74 216
 
5.4%
75 216
 
5.4%

Most occurring characters

ValueCountFrequency (%)
A 2268
13.5%
l 2268
13.5%
5 1944
11.5%
4 1944
11.5%
– 1512
9.0%
1134
6.7%
g 1134
6.7%
e 1134
6.7%
s 1134
6.7%
1 432
 
2.6%
Other values (5) 1944
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6264
37.2%
Lowercase Letter 5670
33.7%
Uppercase Letter 2268
 
13.5%
Dash Punctuation 1512
 
9.0%
Space Separator 1134
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1944
31.0%
4 1944
31.0%
1 432
 
6.9%
2 432
 
6.9%
3 432
 
6.9%
6 432
 
6.9%
7 432
 
6.9%
0 216
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
l 2268
40.0%
g 1134
20.0%
e 1134
20.0%
s 1134
20.0%
Uppercase Letter
ValueCountFrequency (%)
A 2268
100.0%
Dash Punctuation
ValueCountFrequency (%)
– 1512
100.0%
Space Separator
ValueCountFrequency (%)
1134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8910
52.9%
Latin 7938
47.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1944
21.8%
4 1944
21.8%
– 1512
17.0%
1134
12.7%
1 432
 
4.8%
2 432
 
4.8%
3 432
 
4.8%
6 432
 
4.8%
7 432
 
4.8%
0 216
 
2.4%
Latin
ValueCountFrequency (%)
A 2268
28.6%
l 2268
28.6%
g 1134
14.3%
e 1134
14.3%
s 1134
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15336
91.0%
Punctuation 1512
 
9.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2268
14.8%
l 2268
14.8%
5 1944
12.7%
4 1944
12.7%
1134
7.4%
g 1134
7.4%
e 1134
7.4%
s 1134
7.4%
1 432
 
2.8%
2 432
 
2.8%
Other values (4) 1512
9.9%
Punctuation
ValueCountFrequency (%)
– 1512
100.0%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
All Races-All Origins
1404 
Hispanic
486 
Non-Hispanic Black
486 
Non-Hispanic White
486 

Length

Max length21
Median length18
Mean length17.773585
Min length8

Characters and Unicode

Total characters50868
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll Races-All Origins
2nd rowAll Races-All Origins
3rd rowAll Races-All Origins
4th rowAll Races-All Origins
5th rowAll Races-All Origins

Common Values

ValueCountFrequency (%)
All Races-All Origins 1404
49.1%
Hispanic 486
 
17.0%
Non-Hispanic Black 486
 
17.0%
Non-Hispanic White 486
 
17.0%

Length

2023-09-18T22:50:09.614366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-18T22:50:10.090881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
all 1404
21.1%
races-all 1404
21.1%
origins 1404
21.1%
non-hispanic 972
14.6%
hispanic 486
 
7.3%
black 486
 
7.3%
white 486
 
7.3%

Most occurring characters

ValueCountFrequency (%)
i 6210
12.2%
l 6102
12.0%
s 4266
 
8.4%
n 3834
 
7.5%
3780
 
7.4%
a 3348
 
6.6%
c 3348
 
6.6%
A 2808
 
5.5%
- 2376
 
4.7%
e 1890
 
3.7%
Other values (13) 12906
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35694
70.2%
Uppercase Letter 9018
 
17.7%
Space Separator 3780
 
7.4%
Dash Punctuation 2376
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6210
17.4%
l 6102
17.1%
s 4266
12.0%
n 3834
10.7%
a 3348
9.4%
c 3348
9.4%
e 1890
 
5.3%
p 1458
 
4.1%
r 1404
 
3.9%
g 1404
 
3.9%
Other values (4) 2430
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
A 2808
31.1%
H 1458
16.2%
O 1404
15.6%
R 1404
15.6%
N 972
 
10.8%
B 486
 
5.4%
W 486
 
5.4%
Space Separator
ValueCountFrequency (%)
3780
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44712
87.9%
Common 6156
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6210
13.9%
l 6102
13.6%
s 4266
9.5%
n 3834
 
8.6%
a 3348
 
7.5%
c 3348
 
7.5%
A 2808
 
6.3%
e 1890
 
4.2%
H 1458
 
3.3%
p 1458
 
3.3%
Other values (11) 9990
22.3%
Common
ValueCountFrequency (%)
3780
61.4%
- 2376
38.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6210
12.2%
l 6102
12.0%
s 4266
 
8.4%
n 3834
 
7.5%
3780
 
7.4%
a 3348
 
6.6%
c 3348
 
6.6%
A 2808
 
5.5%
- 2376
 
4.7%
e 1890
 
3.7%
Other values (13) 12906
25.4%

Deaths
Categorical

Distinct1602
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
17
 
19
15
 
16
14
 
15
24
 
14
11
 
14
Other values (1597)
2784 

Length

Max length6
Median length5
Mean length3.4318658
Min length1

Characters and Unicode

Total characters9822
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1077 ?
Unique (%)37.6%

Sample

1st row169
2nd row197
3rd row216
4th row211
5th row197

Common Values

ValueCountFrequency (%)
17 19
 
0.7%
15 16
 
0.6%
14 15
 
0.5%
24 14
 
0.5%
11 14
 
0.5%
18 14
 
0.5%
12 13
 
0.5%
26 12
 
0.4%
16 12
 
0.4%
8 11
 
0.4%
Other values (1592) 2722
95.1%

Length

2023-09-18T22:50:10.566653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17 19
 
0.7%
15 16
 
0.6%
14 15
 
0.5%
24 14
 
0.5%
11 14
 
0.5%
18 14
 
0.5%
12 13
 
0.5%
16 12
 
0.4%
26 12
 
0.4%
8 11
 
0.4%
Other values (1592) 2722
95.1%

Most occurring characters

ValueCountFrequency (%)
1 1391
14.2%
2 1148
11.7%
3 1016
10.3%
4 916
9.3%
, 904
9.2%
5 831
8.5%
6 814
8.3%
7 767
7.8%
8 703
7.2%
9 687
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8918
90.8%
Other Punctuation 904
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1391
15.6%
2 1148
12.9%
3 1016
11.4%
4 916
10.3%
5 831
9.3%
6 814
9.1%
7 767
8.6%
8 703
7.9%
9 687
7.7%
0 645
7.2%
Other Punctuation
ValueCountFrequency (%)
, 904
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9822
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1391
14.2%
2 1148
11.7%
3 1016
10.3%
4 916
9.3%
, 904
9.2%
5 831
8.5%
6 814
8.3%
7 767
7.8%
8 703
7.2%
9 687
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9822
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1391
14.2%
2 1148
11.7%
3 1016
10.3%
4 916
9.3%
, 904
9.2%
5 831
8.5%
6 814
8.3%
7 767
7.8%
8 703
7.2%
9 687
7.0%

Population
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct2862
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
4,430,143
 
1
21,087,405
 
1
931,372
 
1
959,656
 
1
983,181
 
1
Other values (2857)
2857 

Length

Max length11
Median length9
Mean length9.2372467
Min length7

Characters and Unicode

Total characters26437
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2862 ?
Unique (%)100.0%

Sample

1st row4,430,143
2nd row4,447,100
3rd row4,467,634
4th row4,480,089
5th row4,503,491

Common Values

ValueCountFrequency (%)
4,430,143 1
 
< 0.1%
21,087,405 1
 
< 0.1%
931,372 1
 
< 0.1%
959,656 1
 
< 0.1%
983,181 1
 
< 0.1%
1,008,427 1
 
< 0.1%
1,034,688 1
 
< 0.1%
1,065,020 1
 
< 0.1%
1,094,335 1
 
< 0.1%
18,236,502 1
 
< 0.1%
Other values (2852) 2852
99.7%

Length

2023-09-18T22:50:10.844249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,430,143 1
 
< 0.1%
626,932 1
 
< 0.1%
648,414 1
 
< 0.1%
642,337 1
 
< 0.1%
4,757,938 1
 
< 0.1%
4,467,634 1
 
< 0.1%
4,480,089 1
 
< 0.1%
4,503,491 1
 
< 0.1%
4,530,729 1
 
< 0.1%
4,569,805 1
 
< 0.1%
Other values (2852) 2852
99.7%

Most occurring characters

ValueCountFrequency (%)
, 5451
20.6%
1 2731
10.3%
2 2467
9.3%
3 2077
 
7.9%
5 2077
 
7.9%
4 2065
 
7.8%
9 2032
 
7.7%
8 1915
 
7.2%
6 1915
 
7.2%
0 1901
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20986
79.4%
Other Punctuation 5451
 
20.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2731
13.0%
2 2467
11.8%
3 2077
9.9%
5 2077
9.9%
4 2065
9.8%
9 2032
9.7%
8 1915
9.1%
6 1915
9.1%
0 1901
9.1%
7 1806
8.6%
Other Punctuation
ValueCountFrequency (%)
, 5451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26437
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 5451
20.6%
1 2731
10.3%
2 2467
9.3%
3 2077
 
7.9%
5 2077
 
7.9%
4 2065
 
7.8%
9 2032
 
7.7%
8 1915
 
7.2%
6 1915
 
7.2%
0 1901
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 5451
20.6%
1 2731
10.3%
2 2467
9.3%
3 2077
 
7.9%
5 2077
 
7.9%
4 2065
 
7.8%
9 2032
 
7.7%
8 1915
 
7.2%
6 1915
 
7.2%
0 1901
 
7.2%

Crude Death Rate
Real number (ℝ)

Distinct2842
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.658853
Minimum0.0389
Maximum68.3122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-18T22:50:11.115420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0389
5-th percentile0.21845
Q13.970475
median9.0742
Q314.895425
95-th percentile27.228515
Maximum68.3122
Range68.2733
Interquartile range (IQR)10.92495

Descriptive statistics

Standard deviation8.5762542
Coefficient of variation (CV)0.80461324
Kurtosis2.8532157
Mean10.658853
Median Absolute Deviation (MAD)5.30285
Skewness1.36343
Sum30505.637
Variance73.552137
MonotonicityNot monotonic
2023-09-18T22:50:11.783279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.9755 2
 
0.1%
0.2151 2
 
0.1%
0.2184 2
 
0.1%
14.5404 2
 
0.1%
0.2012 2
 
0.1%
0.1678 2
 
0.1%
0.2368 2
 
0.1%
6.0794 2
 
0.1%
6.2922 2
 
0.1%
0.1785 2
 
0.1%
Other values (2832) 2842
99.3%
ValueCountFrequency (%)
0.0389 1
< 0.1%
0.0462 1
< 0.1%
0.0538 1
< 0.1%
0.0584 1
< 0.1%
0.0644 1
< 0.1%
0.0657 1
< 0.1%
0.0666 1
< 0.1%
0.0672 1
< 0.1%
0.0686 1
< 0.1%
0.0687 1
< 0.1%
ValueCountFrequency (%)
68.3122 1
< 0.1%
61.698 1
< 0.1%
55.6072 1
< 0.1%
53.4989 1
< 0.1%
51.3474 1
< 0.1%
48.8767 1
< 0.1%
48.658 1
< 0.1%
48.2769 1
< 0.1%
47.8689 1
< 0.1%
47.8431 1
< 0.1%
Distinct2821
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43555598
Minimum0.01428
Maximum2.4078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-18T22:50:12.071707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.01428
5-th percentile0.039794
Q10.19991
median0.36146
Q30.57133
95-th percentile1.0988595
Maximum2.4078
Range2.39352
Interquartile range (IQR)0.37142

Descriptive statistics

Standard deviation0.32763994
Coefficient of variation (CV)0.75223383
Kurtosis2.242235
Mean0.43555598
Median Absolute Deviation (MAD)0.178165
Skewness1.373384
Sum1246.5612
Variance0.10734793
MonotonicityNot monotonic
2023-09-18T22:50:12.371582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03629 2
 
0.1%
0.07886 2
 
0.1%
0.33757 2
 
0.1%
0.24487 2
 
0.1%
0.29935 2
 
0.1%
0.19793 2
 
0.1%
0.43908 2
 
0.1%
0.17244 2
 
0.1%
0.28754 2
 
0.1%
0.16771 2
 
0.1%
Other values (2811) 2842
99.3%
ValueCountFrequency (%)
0.01428 1
< 0.1%
0.01435 1
< 0.1%
0.01578 1
< 0.1%
0.01621 1
< 0.1%
0.01626 1
< 0.1%
0.01649 1
< 0.1%
0.01677 1
< 0.1%
0.0171 1
< 0.1%
0.01729 1
< 0.1%
0.01734 1
< 0.1%
ValueCountFrequency (%)
2.4078 1
< 0.1%
1.79002 1
< 0.1%
1.78726 1
< 0.1%
1.7852 1
< 0.1%
1.76383 1
< 0.1%
1.76103 1
< 0.1%
1.71743 1
< 0.1%
1.70911 1
< 0.1%
1.69938 1
< 0.1%
1.69902 1
< 0.1%
Distinct2834
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8229601
Minimum0.008
Maximum66.8891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-18T22:50:12.664143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.152545
Q13.3927
median8.2098
Q313.723875
95-th percentile26.205305
Maximum66.8891
Range66.8811
Interquartile range (IQR)10.331175

Descriptive statistics

Standard deviation8.3277414
Coefficient of variation (CV)0.84778329
Kurtosis3.0708593
Mean9.8229601
Median Absolute Deviation (MAD)5.03225
Skewness1.4263891
Sum28113.312
Variance69.351277
MonotonicityNot monotonic
2023-09-18T22:50:12.950221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.145 2
 
0.1%
12.1384 2
 
0.1%
3.6576 2
 
0.1%
5.9938 2
 
0.1%
1.5359 2
 
0.1%
0.5728 2
 
0.1%
2.767 2
 
0.1%
10.555 2
 
0.1%
6.4088 2
 
0.1%
1.1786 2
 
0.1%
Other values (2824) 2842
99.3%
ValueCountFrequency (%)
0.008 1
< 0.1%
0.0106 1
< 0.1%
0.012 1
< 0.1%
0.0147 1
< 0.1%
0.0175 1
< 0.1%
0.0183 1
< 0.1%
0.0186 1
< 0.1%
0.0187 1
< 0.1%
0.0194 1
< 0.1%
0.0203 1
< 0.1%
ValueCountFrequency (%)
66.8891 1
< 0.1%
60.2827 1
< 0.1%
52.483 1
< 0.1%
52.2342 1
< 0.1%
48.5497 1
< 0.1%
47.9652 1
< 0.1%
47.8034 1
< 0.1%
46.6901 1
< 0.1%
46.6281 1
< 0.1%
46.3524 1
< 0.1%
Distinct2842
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.55093
Minimum0.0951
Maximum69.7353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-18T22:50:13.245027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0951
5-th percentile0.29555
Q14.67115
median9.9497
Q316.04775
95-th percentile28.56336
Maximum69.7353
Range69.6402
Interquartile range (IQR)11.3766

Descriptive statistics

Standard deviation8.8285716
Coefficient of variation (CV)0.76431695
Kurtosis2.6717251
Mean11.55093
Median Absolute Deviation (MAD)5.49755
Skewness1.3077652
Sum33058.762
Variance77.943677
MonotonicityNot monotonic
2023-09-18T22:50:13.536346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2193 2
 
0.1%
5.5126 2
 
0.1%
6.7191 2
 
0.1%
13.2593 2
 
0.1%
13.379 2
 
0.1%
7.2067 2
 
0.1%
15.0014 2
 
0.1%
4.0272 2
 
0.1%
0.2207 2
 
0.1%
3.0911 2
 
0.1%
Other values (2832) 2842
99.3%
ValueCountFrequency (%)
0.0951 1
< 0.1%
0.1135 1
< 0.1%
0.1295 1
< 0.1%
0.1296 1
< 0.1%
0.1336 1
< 0.1%
0.1359 1
< 0.1%
0.1373 1
< 0.1%
0.1377 1
< 0.1%
0.1476 1
< 0.1%
0.1481 1
< 0.1%
ValueCountFrequency (%)
69.7353 1
< 0.1%
63.1132 1
< 0.1%
58.7315 1
< 0.1%
54.7636 1
< 0.1%
54.1451 1
< 0.1%
51.4594 1
< 0.1%
49.7882 1
< 0.1%
49.5127 1
< 0.1%
49.1098 1
< 0.1%
48.996 1
< 0.1%

Age-adjusted Rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1134
Distinct (%)100.0%
Missing1728
Missing (%)60.4%
Infinite0
Infinite (%)0.0%
Mean11.767422
Minimum1.8205
Maximum52.0211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-18T22:50:13.846724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.8205
5-th percentile3.96827
Q17.521925
median10.95625
Q314.6266
95-th percentile22.510355
Maximum52.0211
Range50.2006
Interquartile range (IQR)7.104675

Descriptive statistics

Standard deviation6.0341026
Coefficient of variation (CV)0.51278034
Kurtosis4.0702959
Mean11.767422
Median Absolute Deviation (MAD)3.4854
Skewness1.4203449
Sum13344.256
Variance36.410394
MonotonicityNot monotonic
2023-09-18T22:50:14.134672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.8521 1
 
< 0.1%
8.3571 1
 
< 0.1%
11.3333 1
 
< 0.1%
8.4571 1
 
< 0.1%
7.3427 1
 
< 0.1%
6.9306 1
 
< 0.1%
6.0588 1
 
< 0.1%
8.4197 1
 
< 0.1%
7.7952 1
 
< 0.1%
14.5328 1
 
< 0.1%
Other values (1124) 1124
39.3%
(Missing) 1728
60.4%
ValueCountFrequency (%)
1.8205 1
< 0.1%
1.8266 1
< 0.1%
1.8774 1
< 0.1%
1.973 1
< 0.1%
2.0354 1
< 0.1%
2.1165 1
< 0.1%
2.1539 1
< 0.1%
2.1657 1
< 0.1%
2.1686 1
< 0.1%
2.3243 1
< 0.1%
ValueCountFrequency (%)
52.0211 1
< 0.1%
41.4936 1
< 0.1%
39.1225 1
< 0.1%
38.9902 1
< 0.1%
38.8 1
< 0.1%
37.8509 1
< 0.1%
36.2896 1
< 0.1%
35.5052 1
< 0.1%
34.3196 1
< 0.1%
33.5499 1
< 0.1%

Standard Error for Age-adjusted Rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1131
Distinct (%)99.7%
Missing1728
Missing (%)60.4%
Infinite0
Infinite (%)0.0%
Mean0.55275706
Minimum0.04671
Maximum2.40658
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-18T22:50:14.439216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.04671
5-th percentile0.090265
Q10.26312
median0.46644
Q30.742965
95-th percentile1.398227
Maximum2.40658
Range2.35987
Interquartile range (IQR)0.479845

Descriptive statistics

Standard deviation0.39833836
Coefficient of variation (CV)0.72063911
Kurtosis1.1206279
Mean0.55275706
Median Absolute Deviation (MAD)0.228545
Skewness1.1659438
Sum626.82651
Variance0.15867345
MonotonicityNot monotonic
2023-09-18T22:50:14.781792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.58859 2
 
0.1%
0.57484 2
 
0.1%
0.85054 2
 
0.1%
0.29657 1
 
< 0.1%
0.32693 1
 
< 0.1%
0.46675 1
 
< 0.1%
0.44161 1
 
< 0.1%
0.38228 1
 
< 0.1%
0.35701 1
 
< 0.1%
0.34809 1
 
< 0.1%
Other values (1121) 1121
39.2%
(Missing) 1728
60.4%
ValueCountFrequency (%)
0.04671 1
< 0.1%
0.04682 1
< 0.1%
0.04881 1
< 0.1%
0.05255 1
< 0.1%
0.05336 1
< 0.1%
0.05349 1
< 0.1%
0.05537 1
< 0.1%
0.05552 1
< 0.1%
0.0565 1
< 0.1%
0.05681 1
< 0.1%
ValueCountFrequency (%)
2.40658 1
< 0.1%
2.0475 1
< 0.1%
1.90026 1
< 0.1%
1.87901 1
< 0.1%
1.82691 1
< 0.1%
1.81892 1
< 0.1%
1.80703 1
< 0.1%
1.79198 1
< 0.1%
1.7867 1
< 0.1%
1.78648 1
< 0.1%

Lower Confidence Limit for Age-adjusted Rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1128
Distinct (%)99.5%
Missing1728
Missing (%)60.4%
Infinite0
Infinite (%)0.0%
Mean10.701018
Minimum0.9407
Maximum48.5088
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-18T22:50:15.096673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.9407
5-th percentile3.186125
Q16.6791
median10.021
Q313.335475
95-th percentile21.01467
Maximum48.5088
Range47.5681
Interquartile range (IQR)6.656375

Descriptive statistics

Standard deviation5.7388486
Coefficient of variation (CV)0.53628997
Kurtosis3.896313
Mean10.701018
Median Absolute Deviation (MAD)3.3361
Skewness1.3911107
Sum12134.954
Variance32.934383
MonotonicityNot monotonic
2023-09-18T22:50:15.427522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.1657 2
 
0.1%
11.3061 2
 
0.1%
13.8507 2
 
0.1%
8.7679 2
 
0.1%
6.4074 2
 
0.1%
17.7399 2
 
0.1%
20.9964 1
 
< 0.1%
11.922 1
 
< 0.1%
4.8911 1
 
< 0.1%
7.1278 1
 
< 0.1%
Other values (1118) 1118
39.1%
(Missing) 1728
60.4%
ValueCountFrequency (%)
0.9407 1
< 0.1%
0.9438 1
< 0.1%
1.127 1
< 0.1%
1.1392 1
< 0.1%
1.184 1
< 0.1%
1.2707 1
< 0.1%
1.3785 1
< 0.1%
1.4063 1
< 0.1%
1.4727 1
< 0.1%
1.5228 1
< 0.1%
ValueCountFrequency (%)
48.5088 1
< 0.1%
38.3938 1
< 0.1%
37.929 1
< 0.1%
36.7315 1
< 0.1%
35.4095 1
< 0.1%
34.0831 1
< 0.1%
33.4134 1
< 0.1%
32.6581 1
< 0.1%
32.3561 1
< 0.1%
31.7688 1
< 0.1%

Upper Confidence Limit for Age-adjusted Rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1132
Distinct (%)99.8%
Missing1728
Missing (%)60.4%
Infinite0
Infinite (%)0.0%
Mean12.886507
Minimum2.2101
Maximum55.5334
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-18T22:50:15.763618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.2101
5-th percentile4.571045
Q18.45745
median12.0529
Q315.892625
95-th percentile24.64935
Maximum55.5334
Range53.3233
Interquartile range (IQR)7.435175

Descriptive statistics

Standard deviation6.3860797
Coefficient of variation (CV)0.49556328
Kurtosis4.1129831
Mean12.886507
Median Absolute Deviation (MAD)3.70755
Skewness1.43263
Sum14613.299
Variance40.782014
MonotonicityNot monotonic
2023-09-18T22:50:16.097843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.1031 2
 
0.1%
13.0844 2
 
0.1%
4.4334 1
 
< 0.1%
10.7317 1
 
< 0.1%
13.7516 1
 
< 0.1%
12.1988 1
 
< 0.1%
9.2064 1
 
< 0.1%
8.0425 1
 
< 0.1%
7.6129 1
 
< 0.1%
6.6995 1
 
< 0.1%
Other values (1122) 1122
39.2%
(Missing) 1728
60.4%
ValueCountFrequency (%)
2.2101 1
< 0.1%
2.3924 1
< 0.1%
2.4204 1
< 0.1%
2.4557 1
< 0.1%
2.942 1
< 0.1%
3.0466 1
< 0.1%
3.1208 1
< 0.1%
3.1801 1
< 0.1%
3.1907 1
< 0.1%
3.1969 1
< 0.1%
ValueCountFrequency (%)
55.5334 1
< 0.1%
44.5934 1
< 0.1%
43.5169 1
< 0.1%
42.571 1
< 0.1%
40.316 1
< 0.1%
39.1658 1
< 0.1%
38.9703 1
< 0.1%
38.3523 1
< 0.1%
37.682 1
< 0.1%
35.3309 1
< 0.1%

State Crude Rate in Range
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct60
Distinct (%)5.3%
Missing1728
Missing (%)60.4%
Memory size22.5 KiB
10.3–12.6
188 
1.8–7.1
187 
12.6–16
187 
16–52
187 
7.1–10.2
187 
Other values (55)
198 

Length

Max length9
Median length8
Mean length7.2283951
Min length5

Characters and Unicode

Total characters8197
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.8–7.1
2nd row1.8–7.1
3rd row1.8–7.1
4th row1.8–7.1
5th row1.8–7.1

Common Values

ValueCountFrequency (%)
10.3–12.6 188
 
6.6%
1.8–7.1 187
 
6.5%
12.6–16 187
 
6.5%
16–52 187
 
6.5%
7.1–10.2 187
 
6.5%
6.2–6.5 4
 
0.1%
11.2–11.5 4
 
0.1%
2.7–3 4
 
0.1%
3.1–3.5 4
 
0.1%
3.6–4.1 4
 
0.1%
Other values (50) 178
 
6.2%
(Missing) 1728
60.4%

Length

2023-09-18T22:50:16.412491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10.3–12.6 188
16.6%
1.8–7.1 187
16.5%
12.6–16 187
16.5%
16–52 187
16.5%
7.1–10.2 187
16.5%
9.3–10.5 4
 
0.4%
8.3–8.7 4
 
0.4%
5.4–5.6 4
 
0.4%
19–21.4 4
 
0.4%
5.8–5.9 4
 
0.4%
Other values (50) 178
15.7%

Most occurring characters

ValueCountFrequency (%)
1 1906
23.3%
. 1657
20.2%
– 1134
13.8%
6 849
10.4%
2 840
10.2%
7 457
 
5.6%
0 400
 
4.9%
5 282
 
3.4%
8 279
 
3.4%
3 276
 
3.4%
Other values (2) 117
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5406
66.0%
Other Punctuation 1657
 
20.2%
Dash Punctuation 1134
 
13.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1906
35.3%
6 849
15.7%
2 840
15.5%
7 457
 
8.5%
0 400
 
7.4%
5 282
 
5.2%
8 279
 
5.2%
3 276
 
5.1%
4 64
 
1.2%
9 53
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 1657
100.0%
Dash Punctuation
ValueCountFrequency (%)
– 1134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8197
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1906
23.3%
. 1657
20.2%
– 1134
13.8%
6 849
10.4%
2 840
10.2%
7 457
 
5.6%
0 400
 
4.9%
5 282
 
3.4%
8 279
 
3.4%
3 276
 
3.4%
Other values (2) 117
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7063
86.2%
Punctuation 1134
 
13.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1906
27.0%
. 1657
23.5%
6 849
12.0%
2 840
11.9%
7 457
 
6.5%
0 400
 
5.7%
5 282
 
4.0%
8 279
 
4.0%
3 276
 
3.9%
4 64
 
0.9%
Punctuation
ValueCountFrequency (%)
– 1134
100.0%

US Crude Rate
Real number (ℝ)

Distinct18
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.481156
Minimum6.0382
Maximum19.6925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-18T22:50:16.687023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.0382
5-th percentile6.0382
Q18.8881
median11.97035
Q313.2674
95-th percentile19.6925
Maximum19.6925
Range13.6543
Interquartile range (IQR)4.3793

Descriptive statistics

Standard deviation3.4870832
Coefficient of variation (CV)0.30372232
Kurtosis-0.14569439
Mean11.481156
Median Absolute Deviation (MAD)2.27335
Skewness0.3281087
Sum32859.067
Variance12.159749
MonotonicityNot monotonic
2023-09-18T22:50:16.957840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
6.0382 159
 
5.6%
6.1882 159
 
5.6%
16.304 159
 
5.6%
14.7574 159
 
5.6%
13.9127 159
 
5.6%
13.2208 159
 
5.6%
13.2674 159
 
5.6%
12.4144 159
 
5.6%
12.0624 159
 
5.6%
11.9864 159
 
5.6%
Other values (8) 1272
44.4%
ValueCountFrequency (%)
6.0382 159
5.6%
6.1882 159
5.6%
6.8057 159
5.6%
8.1766 159
5.6%
8.8881 159
5.6%
9.366 159
5.6%
10.0884 159
5.6%
11.5373 159
5.6%
11.9543 159
5.6%
11.9864 159
5.6%
ValueCountFrequency (%)
19.6925 159
5.6%
16.304 159
5.6%
14.7574 159
5.6%
13.9127 159
5.6%
13.2674 159
5.6%
13.2208 159
5.6%
12.4144 159
5.6%
12.0624 159
5.6%
11.9864 159
5.6%
11.9543 159
5.6%

US Age-adjusted Rate
Real number (ℝ)

Distinct18
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.440756
Minimum6.057
Maximum19.7851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-18T22:50:17.226689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.057
5-th percentile6.057
Q18.8765
median11.8861
Q313.1852
95-th percentile19.7851
Maximum19.7851
Range13.7281
Interquartile range (IQR)4.3087

Descriptive statistics

Standard deviation3.4815601
Coefficient of variation (CV)0.30431208
Kurtosis-0.041457994
Mean11.440756
Median Absolute Deviation (MAD)2.2087
Skewness0.3805173
Sum32743.442
Variance12.121261
MonotonicityNot monotonic
2023-09-18T22:50:17.477751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
6.057 159
 
5.6%
6.1749 159
 
5.6%
16.2923 159
 
5.6%
14.6831 159
 
5.6%
13.8005 159
 
5.6%
13.1422 159
 
5.6%
13.1852 159
 
5.6%
12.2966 159
 
5.6%
11.9388 159
 
5.6%
11.8947 159
 
5.6%
Other values (8) 1272
44.4%
ValueCountFrequency (%)
6.057 159
5.6%
6.1749 159
5.6%
6.7922 159
5.6%
8.1957 159
5.6%
8.8765 159
5.6%
9.3831 159
5.6%
10.0699 159
5.6%
11.4883 159
5.6%
11.8775 159
5.6%
11.8947 159
5.6%
ValueCountFrequency (%)
19.7851 159
5.6%
16.2923 159
5.6%
14.6831 159
5.6%
13.8005 159
5.6%
13.1852 159
5.6%
13.1422 159
5.6%
12.2966 159
5.6%
11.9388 159
5.6%
11.8947 159
5.6%
11.8775 159
5.6%

Unit
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
per 100,000 population
2862 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters62964
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowper 100,000 population
2nd rowper 100,000 population
3rd rowper 100,000 population
4th rowper 100,000 population
5th rowper 100,000 population

Common Values

ValueCountFrequency (%)
per 100,000 population 2862
100.0%

Length

2023-09-18T22:50:17.777737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-18T22:50:18.047969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
per 2862
33.3%
100,000 2862
33.3%
population 2862
33.3%

Most occurring characters

ValueCountFrequency (%)
0 14310
22.7%
p 8586
13.6%
5724
 
9.1%
o 5724
 
9.1%
e 2862
 
4.5%
r 2862
 
4.5%
1 2862
 
4.5%
, 2862
 
4.5%
u 2862
 
4.5%
l 2862
 
4.5%
Other values (4) 11448
18.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37206
59.1%
Decimal Number 17172
27.3%
Space Separator 5724
 
9.1%
Other Punctuation 2862
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 8586
23.1%
o 5724
15.4%
e 2862
 
7.7%
r 2862
 
7.7%
u 2862
 
7.7%
l 2862
 
7.7%
a 2862
 
7.7%
t 2862
 
7.7%
i 2862
 
7.7%
n 2862
 
7.7%
Decimal Number
ValueCountFrequency (%)
0 14310
83.3%
1 2862
 
16.7%
Space Separator
ValueCountFrequency (%)
5724
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2862
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37206
59.1%
Common 25758
40.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 8586
23.1%
o 5724
15.4%
e 2862
 
7.7%
r 2862
 
7.7%
u 2862
 
7.7%
l 2862
 
7.7%
a 2862
 
7.7%
t 2862
 
7.7%
i 2862
 
7.7%
n 2862
 
7.7%
Common
ValueCountFrequency (%)
0 14310
55.6%
5724
 
22.2%
1 2862
 
11.1%
, 2862
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14310
22.7%
p 8586
13.6%
5724
 
9.1%
o 5724
 
9.1%
e 2862
 
4.5%
r 2862
 
4.5%
1 2862
 
4.5%
, 2862
 
4.5%
u 2862
 
4.5%
l 2862
 
4.5%
Other values (4) 11448
18.2%

Interactions

2023-09-18T22:50:02.312550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:29.600032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:32.443861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:36.610486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:39.405747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:43.530446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:46.638873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:49.680711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:52.459236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:56.113965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:59.499275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:02.594239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:29.876340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:32.705590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:36.864329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:39.673363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:43.787360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:46.886811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:49.932287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:52.709236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:56.526349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:59.758963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:02.846669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:30.137413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:32.960170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:37.126227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:40.047372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:44.061344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:47.143308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:50.186660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:52.952540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:56.939232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:00.015316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:03.098479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:30.396381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:33.207000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:37.360687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:40.458147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:44.315562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:47.388900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:50.423003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:53.208222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:57.190523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:00.262418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:03.355630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:30.669591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:33.504561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:37.632023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:40.822099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:44.571417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:47.638825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:50.663851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:53.457849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:57.442622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:00.543536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:03.623639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:30.924440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:33.774032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:37.900082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:41.184754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:44.852537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:47.917966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:50.908999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:53.820520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:57.995886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:00.806634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:03.858689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:31.166802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:34.021461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:38.134174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:41.568544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:45.087566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:48.256118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:51.159039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:54.207647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:58.227520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:01.048354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:04.117134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:31.431444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:34.264296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:38.383752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:41.958510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:45.596877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:48.512667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:51.407582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:54.607755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:58.490815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:01.295983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:04.364702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:31.686308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:35.828939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:38.641384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:42.349768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:45.841101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:48.885858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:51.664880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:54.995743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:58.734957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:01.574442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:04.641099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:31.924818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:36.055876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:38.891176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:42.756435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:46.088748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:49.135592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:51.923656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:55.409561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:58.971535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:01.821219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:04.895800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:32.180029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:36.329073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:39.145636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:43.202269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:46.365268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:49.411218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:52.199674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:55.756322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:49:59.226232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:50:02.066902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-18T22:50:18.248097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
YearCrude Death RateStandard Error for Crude RateLower Confidence Limit for Crude RateUpper Confidence Limit for Crude RateAge-adjusted RateStandard Error for Age-adjusted RateLower Confidence Limit for Age-adjusted RateUpper Confidence Limit for Age-adjusted RateUS Crude RateUS Age-adjusted RateStateSexAge GroupRace and Hispanic OriginState Crude Rate in Range
Year1.0000.3330.1340.3310.3330.6170.1900.6150.6070.9980.9980.0000.0000.0000.0000.247
Crude Death Rate0.3331.0000.4650.9970.9960.9980.3560.9880.9870.3330.3330.1520.2490.2840.2200.505
Standard Error for Crude Rate0.1340.4651.0000.4080.5290.3600.9990.2420.4730.1340.1340.4340.1930.1980.1930.224
Lower Confidence Limit for Crude Rate0.3310.9970.4081.0000.9870.9880.2410.9980.9580.3310.3310.1600.2380.2780.2180.492
Upper Confidence Limit for Crude Rate0.3330.9960.5290.9871.0000.9890.4650.9610.9970.3330.3330.1580.2520.2880.2160.457
Age-adjusted Rate0.6170.9980.3600.9880.9891.0000.3620.9900.9900.6170.6170.2740.1861.0000.1600.505
Standard Error for Age-adjusted Rate0.1900.3560.9990.2410.4650.3621.0000.2440.4750.1900.1900.5200.4061.0000.3540.182
Lower Confidence Limit for Age-adjusted Rate0.6150.9880.2420.9980.9610.9900.2441.0000.9600.6150.6150.2600.1531.0000.1380.503
Upper Confidence Limit for Age-adjusted Rate0.6070.9870.4730.9580.9970.9900.4750.9601.0000.6070.6070.2750.2011.0000.1690.488
US Crude Rate0.9980.3330.1340.3310.3330.6170.1900.6150.6071.0001.0000.0000.0000.0000.0000.301
US Age-adjusted Rate0.9980.3330.1340.3310.3330.6170.1900.6150.6071.0001.0000.0000.0000.0000.0000.276
State0.0000.1520.4340.1600.1580.2740.5200.2600.2750.0000.0001.0000.4220.2690.3820.000
Sex0.0000.2490.1930.2380.2520.1860.4060.1530.2010.0000.0000.4221.0000.3710.3080.974
Age Group0.0000.2840.1980.2780.2881.0001.0001.0001.0000.0000.0000.2690.3711.0000.3391.000
Race and Hispanic Origin0.0000.2200.1930.2180.2160.1600.3540.1380.1690.0000.0000.3820.3080.3391.0000.975
State Crude Rate in Range0.2470.5050.2240.4920.4570.5050.1820.5030.4880.3010.2760.0000.9741.0000.9751.000

Missing values

2023-09-18T22:50:05.294842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-18T22:50:05.927383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-18T22:50:06.346846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

StateYearSexAge GroupRace and Hispanic OriginDeathsPopulationCrude Death RateStandard Error for Crude RateLower Confidence Limit for Crude RateUpper Confidence Limit for Crude RateAge-adjusted RateStandard Error for Age-adjusted RateLower Confidence Limit for Age-adjusted RateUpper Confidence Limit for Age-adjusted RateState Crude Rate in RangeUS Crude RateUS Age-adjusted RateUnit
0Alabama1999Both SexesAll AgesAll Races-All Origins1694,430,1433.81480.293443.23964.38993.85210.296573.27084.43341.8–7.16.03826.0570per 100,000 population
1Alabama2000Both SexesAll AgesAll Races-All Origins1974,447,1004.42990.315613.81125.04854.48570.319853.85885.11261.8–7.16.18826.1749per 100,000 population
2Alabama2001Both SexesAll AgesAll Races-All Origins2164,467,6344.83480.328964.19005.47954.89150.333294.23825.54471.8–7.16.80576.7922per 100,000 population
3Alabama2002Both SexesAll AgesAll Races-All Origins2114,480,0894.70970.324234.07425.34524.76190.328684.11775.40621.8–7.18.17668.1957per 100,000 population
4Alabama2003Both SexesAll AgesAll Races-All Origins1974,503,4914.37440.311663.76354.98524.43330.317013.81205.05471.8–7.18.88818.8765per 100,000 population
5Alabama2004Both SexesAll AgesAll Races-All Origins2834,530,7296.24620.371305.51856.97406.35420.379445.61057.09791.8–7.19.36609.3831per 100,000 population
6Alabama2005Both SexesAll AgesAll Races-All Origins2834,569,8056.19280.368135.47136.91436.33300.378325.59157.07451.8–7.110.088410.0699per 100,000 population
7Alabama2006Both SexesAll AgesAll Races-All Origins3984,628,9818.59800.430987.75339.44278.74980.441627.88429.61547.1–10.211.537311.4883per 100,000 population
8Alabama2007Both SexesAll AgesAll Races-All Origins5114,672,84010.93550.483769.987411.883711.08850.4951610.118012.059010.3–12.611.954311.8775per 100,000 population
9Alabama2008Both SexesAll AgesAll Races-All Origins6074,718,20612.86510.5221811.841613.888512.98110.5329211.936614.025612.6–1611.986411.8947per 100,000 population
StateYearSexAge GroupRace and Hispanic OriginDeathsPopulationCrude Death RateStandard Error for Crude RateLower Confidence Limit for Crude RateUpper Confidence Limit for Crude RateAge-adjusted RateStandard Error for Age-adjusted RateLower Confidence Limit for Age-adjusted RateUpper Confidence Limit for Age-adjusted RateState Crude Rate in RangeUS Crude RateUS Age-adjusted RateUnit
2852Wyoming2007Both SexesAll AgesAll Races-All Origins63534,87611.77841.483949.050915.069712.26661.574489.383015.757010.3–12.611.954311.8775per 100,000 population
2853Wyoming2008Both SexesAll AgesAll Races-All Origins74546,04313.55201.5753910.641317.013414.04531.6644010.969517.716212.6–1611.986411.8947per 100,000 population
2854Wyoming2009Both SexesAll AgesAll Races-All Origins60559,85110.71711.383588.178313.795110.95081.440878.315414.156410.3–12.612.062411.9388per 100,000 population
2855Wyoming2010Both SexesAll AgesAll Races-All Origins85563,62615.08091.6357612.046118.647814.95561.6626111.876918.588412.6–1612.414412.2966per 100,000 population
2856Wyoming2011Both SexesAll AgesAll Races-All Origins85568,15814.96061.6227111.950018.499015.20821.6945412.077518.902412.6–1613.267413.1852per 100,000 population
2857Wyoming2012Both SexesAll AgesAll Races-All Origins98576,41217.00171.7174313.802820.719716.75071.7378213.520020.520816–5213.220813.1422per 100,000 population
2858Wyoming2013Both SexesAll AgesAll Races-All Origins98582,65816.81951.6990213.654920.497517.24221.7864813.916721.122916–5213.912713.8005per 100,000 population
2859Wyoming2014Both SexesAll AgesAll Races-All Origins109584,15318.65951.7872615.156522.162519.38231.9002615.657723.106816–5214.757414.6831per 100,000 population
2860Wyoming2015Both SexesAll AgesAll Races-All Origins96586,10716.37931.6717013.267320.001916.44541.7294013.224120.214216–5216.304016.2923per 100,000 population
2861Wyoming2016Both SexesAll AgesAll Races-All Origins99585,50116.90861.6993813.742520.585617.58711.8189214.195021.545316–5219.692519.7851per 100,000 population